Probabilistic matrix factorization (PMF) and other popular approaches to collaborative filtering assume that the ratings given by users for products are genuine, and hence they give equal importance to all available ratings. However, this is not always true due to several reasons including the presence of opinion spam in product reviews. The proposed systems identify malicious feedback ratings by assuming the Cumulative Sum Control Chart, and then it diminishes the effect of individual user feedback references retaining the Pearson Correlation Coefficient. This system gives demand to preserve malicious feedback ratings and it suggest a malicious feedback rating prevention scheme employing Bloom filtering to increase the recommendation achievement. The consequences show that the planned measurement method can lessen the deviance of the reputation measurement and improve the success ratio of the Web service recommendation. Reputation of Web services is a widely-employed metric that determines whether the service should be recommended to a user. Extensive experiments re conducted by employing a real feedback rating dataset with 1.5 million Web service invocation records. The experimental results show that our proposed method can reduce the deviation of the reputation measurement and enhance the success ratio of the Web service recommendation.